Multidisciplinary project involving natural language processing and machine learning for clinical language processing

重点是提议自动发现不同疾病之间的相似之处，这些相似之处可能导致候选疾病进行联合治疗。为了超越可能在临床文献中广为人知的疾病组合，该建议旨在在大范围内详尽地比较疾病之间的相似性，以确定可能的候选组合。这涉及从包括医学分类法和百科全书在内的来源中提取有关疾病的信息，并为每种情况及其属性创建一个表示，以便进行比较。此外，根据疾病的共同特征，包括症状、治疗和演变，识别表示之间的相似性的技术将被用于大规模比较疾病。这些将使发现具有相似特征并可能进行联合治疗的疾病的候选组合成为可能。

The focus is on proposing to automatically discover similarities among different morbidities that could lead to candidate illnesses for joint treatment. To go beyond combinations of diseases that may be well known in the clinical literature, this proposal aims to exhaustively compare similarities between diseases at a large scale to identify possible candidate combinations. This involves extracting relevant information about diseases from sources including medical taxonomies and encyclopedias and creating a representation for each of the conditions and their properties that allows for their comparison. In addition, techniques for identifying similarities between representations will be used for large-scale comparison of diseases according to their common characteristics, including symptoms, treatments and evolution. These will enable the discovery of candidate combinations of diseases that have similar characteristics and could have joint treatments.